Bike Sharing Statistical Analysis

About

In this project, I am using Python to explore data on bike share systems for three major cities in the US - Chicago, New York, and Washington. I wrote code to import data and answer interesting questions about it by calculating descriptive statistics. I also wrote a script that takes raw input to create an interactive in-device experience to present these stats.

Data

• Start Time (e.g., 2017-01-01 00:07:57)
• End Time (e.g., 2017-01-01 00:20:53)
• Trip Duration (in seconds - e.g., 776)
• Start Station (e.g., Broadway & Barry Ave)
• End Station (e.g., Sedgwick St & North Ave)
• User Type (Subscriber or Customer)

The Chicago and New York City files also have the following two columns:

• Gender
• Birth Year

Statistics required

Popular times of travel (i.e., occurs most often in the start time)

• most common month

• most common day of week

• most common hour of day

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Popular stations and trip

• most common start station

• most common end station

• most common trip from start to end (i.e., most frequent combination of start station and end station)

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Trip duration

• total travel time

• average travel time

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User info

• counts of each user type

• counts of each gender (only available for NYC and Chicago)

• earliest, most recent, most common year of birth (only available for NYC and Chicago)

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display this statistics with visuals

• Bar chart for

  • Hours count
  • Months count
  • Days count
  • Start stations count
  • End stations count

• Pie chart for

  • Usre types
  • Genders

• Hist for

  • Birthdays

tools and skills used

• descriptive statistics
• python programming language
• libraries matplotlib, numpy, pandas, and seaborn
• VScode
• Markdown

How to install and use this code?

• Download the script

• Download python Link

• Install: pandas, matplotlib, seaborn, and numpy

  pip install pandas
  pip install seaborn
  pip install matplotlib
  pip install numpy